JP6025821B2 - Method for outputting estimated QoE on terminal for each application - Google Patents

Description

  Unless otherwise stated herein, the approaches described in this section are not prior art to the claims in this application and are prior art by inclusion in this section. It is not necessarily accepted.

  For users who want to use a particular application on the terminal, it is useful to know how much QoE can be obtained when that application is actually executed on the terminal.

  Therefore, it is desired to provide a useful method for outputting information on the estimated QoE on the terminal.

  According to one embodiment, a method is provided for outputting information on estimated QoE on a terminal. The method may include performing QoE estimation for each application and outputting information on the estimated QoE on the terminal based on the QoE estimation.

  The subject matter is pointed out in detail and explicitly claimed in the concluding portion of the specification. The foregoing and other features of the present disclosure will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Through the use of the accompanying drawings, it will be understood that these drawings depict only some embodiments according to the present disclosure and therefore should not be considered as limiting the scope of the present disclosure. The present disclosure will be described with additional specificity and detail.

It is a conceptual diagram which shows cloud computing. 4 is a flowchart for illustrating a process executed by a terminal 10. 4 is a diagram for illustrating an example of information displayed on a display 12 of the terminal 10. FIG. It is a figure for showing an example of QoE estimation regarding application "web album." It is a figure for showing an example of QoE estimation regarding application "video delivery." It is a figure for showing an example of QoE estimation regarding application "IP telephone." It is a flowchart for showing an example of the method of setting the reference | standard used when estimating QoE. 3 is a functional block diagram for illustrating an example of a configuration of a terminal 10. FIG. 5 is a flowchart for illustrating an example of operations of a subsequent action prediction unit 102 and a QoE estimation triggering unit 104. 10 is a flowchart for illustrating another example of operations of the subsequent action prediction unit 102 and the QoE estimation triggering unit 104.

  The following description presents various examples along with specific details in order to provide a thorough understanding of claimed subject matter. However, one of ordinary skill in the art appreciates that the claimed subject matter can be practiced without some or more of the specific details disclosed herein. . Further, in some situations, well-known methods, procedures, systems, components, and / or circuits have been described in detail in order to avoid unnecessarily obscuring the claimed subject matter. Not explained.

  In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized and other changes may be made without departing from the spirit or scope of the subject matter presented herein. The aspects of the present disclosure that are generally described herein and illustrated in the figures can be arranged, substituted, combined, and designed in a variety of different configurations, and It will be readily appreciated that all of the configurations are expressly considered part of this disclosure and are part of this disclosure.

  FIG. 1 shows a conceptual diagram of cloud computing. As shown in FIG. 1, the user of the terminal 10 can enjoy various applications such as e-mail and calendar in or through the cloud 20.

  The terminal 10 may be of any type. The terminal 10 may be a smartphone, a tablet, a laptop computer, a desktop computer, or the like.

  The cloud 20 may be an inclusive element that includes multiple elements of a communication network accessed by the terminal 10. In other words, the cloud 20 can include multiple elements that allow an application to run on the terminal 10. The cloud 20 may include the Internet and various service providers such as [Google], [Yahoo], and the like.

  The terminal 10 can be configured to perform QoE estimation for each application. Specifically, the terminal 10 can be configured to estimate, for each application, QoE (Quality of Experience) related to each application that can be executed on the terminal 10.

  Here, the QoE (Quality of Experience) of the application may depend on factors such as the status of the wireless access network, the status of the core network, and the internal status of the cloud 20. QoE can indicate the quality of communication required by the user. Thus, QoE may vary from application to application. Further, the QoE may be different for each user.

  There are various ways to estimate the QoE for an application. Any method can be used as long as QoE can be estimated for each application. Some examples of how to estimate QoE for an application are described below.

  The terminal 10 can be configured to output information regarding the estimated QoE of each application on the display of the terminal. This information can be output in various forms. For example, the estimated QoE of each application can be displayed directly on the terminal display. Some examples of how to output information about the estimated QoE for each application are described below.

  FIG. 2 is a flowchart for illustrating a process executed by the terminal 10.

  In step 200, QoE estimation can be performed for each application. For example, if there are four applications, QoE estimation can be performed separately for each of those four applications. The timing of QoE estimation for each of the four applications may be arbitrary. For example, the QoE estimation for each of the four applications can be performed continuously as a batch process or can be performed at intervals over time.

  QoE estimation may be performed using a criterion. This criterion may vary from application to application. Therefore, this standard may be set for each application. This criterion may be set in advance as a default criterion. This reference may be set based on user data of the terminal. The user data can include information indicating the needs or preferences of the user of the terminal 10. Such information may be statistically derived by the user from data obtained during the actual operation of the terminal 10 or entered directly by the user. For example, the default criteria can be corrected according to input from the user.

  In step 202, information regarding the estimated QoE of each application can be output on the terminal display. Information regarding the estimated QoE of the application can be output (updated) when the QoE estimation for the application is completed. Therefore, information regarding the estimated QoE of each application may be output (updated) at different timings. Information regarding the estimated QoE of the application may be output (updated) before the application is actually executed on the terminal 10.

  FIG. 3 is a diagram for illustrating an example of information displayed on the display 12 of the terminal 10. In FIG. 3, as an example, seven applications (e-mail, web album, map, video distribution, IP phone, calendar, and street view) can be executed on the terminal 10.

  As shown in FIG. 3, the terminal 10 can be configured to display icons 14a-14g. The number of icons can depend on the number of applications. Icons 14a-14g can be provided to start the corresponding application. In other words, the icons 14a to 14g may be launchers for corresponding applications. Each application can be started when the corresponding icon 14a-14g is activated. The activation of the icons 14a to 14g can be performed by a click operation, a touch operation or the like. Icons 14a-14g may include letters or illustrations indicating the corresponding application, as shown in FIG.

  The terminal 10 can be configured to change the color of the icon displayed on the terminal 10 according to the QoE estimation. For example, “blue” may indicate a “very good” QoE, “green” may indicate a “good” QoE, and “yellow” may indicate a “bad” QoE. “Red” can indicate a QoE of “bad”, and “Gray” can indicate a QoE of “unusable”. For example, if the estimated QoE for application “E-mail” associated with icon 14a is “good”, the color of icon 14a can be changed to yellow. In this configuration, the estimated QoE can be easily distinguished by color.

  Note that icons 14a-14g can be replaced by links in the case of websites or emails. Links are often provided on websites or emails. In this case, the link functions as a launcher for executing the application. Thus, in this case, the status link, such as the color of the link, can be changed according to the estimated QoE of the corresponding application.

  FIG. 4 is a diagram illustrating an example of QoE estimation related to the application “web album”. The application “web album” can be said to be an application for browsing (viewing) images (photos) in the cloud 20. The application “Web Album” can be used to share specific images with specific parties such as friends and family.

  QoE estimation for the application “Web Album” can be performed by the following steps 1 to 3.

  In step 1, the terminal 10 can request browsing of images in the cloud 20. Specifically, the terminal 10 can issue a request for the image test data to the cloud 20. The test data may be part of a normal image, as schematically shown in FIG. In other words, the test data may have a data size smaller than that of a normal image. Test data may be specially prepared for QoE estimation.

  In step 2, the cloud 20 can transfer the requested image to the terminal 10. In other words, the terminal 10 can receive image test data from the cloud 20.

  In step 3, the terminal 10 can measure the transfer time between the request and the reception of the image test data at the terminal 10.

  In step 4, the terminal 10 can estimate the QoE for the application “Web Album” based on the measured transfer time. For example, the relationship between the measured transfer time and the reference can be said to be the basis for estimating QoE. If the measured transfer time is sufficiently shorter than the reference transfer time, the QoE can be estimated to be “very good”. If the measured transfer time is shorter than the reference transfer time, but not sufficiently short, the QoE can be estimated to be “good”. Similarly, QoE can be estimated to be “bad” if the measured transfer time is significantly longer than the transfer time according to the criteria. If the measured transfer time is longer than the reference transfer time, but not significantly longer, the QoE can be estimated to be “bad”. If the image test data cannot be received at the terminal 10, the QoE can be estimated as “unusable”. If the estimated QoE is high, such as “very good” or “good”, the user can be encouraged to view a high quality image. On the other hand, if the estimated QoE is low, such as “bad”, the user can be encouraged to view a low quality image.

  FIG. 5 is a diagram for illustrating an example of QoE estimation related to the application “video distribution”. The application “video distribution” can be said to be an application for browsing (viewing) a video in the cloud 20. The application “video distribution” may be [YouTube] or the like.

  QoE estimation for the application “video delivery” can be performed by the following steps 1-3.

  In step 1, the terminal 10 can request browsing of the video in the cloud 20. Specifically, the terminal 10 can issue a request for video test data to the cloud 20. The test data may be part of a normal video, as schematically shown in FIG. In other words, the test data may have a data size smaller than that of normal video. Test data may be specially prepared for QoE estimation.

  In step 2, the cloud 20 can transfer the requested video to the terminal 10. In other words, the terminal 10 can receive video test data from the cloud 20.

  In step 3, the terminal 10 can measure the transfer time between requesting and receiving video test data at the terminal 10. The terminal 10 can also measure the packet loss rate during transfer.

  In step 4, the terminal 10 can estimate the QoE for the application “video delivery” based on the measured transfer time and packet loss rate. For example, the relationship between the measured transfer time and packet loss rate and the reference may be the basis for estimating QoE. If the measured transfer time and packet loss rate are well below the reference transfer time and packet loss rate, the QoE can be estimated to be “very good”. If the measured transfer time and packet loss rate are smaller than the reference transfer time and packet loss rate, but not sufficiently small, QoE can be estimated to be “good”. . Similarly, if the measured transfer time and packet loss rate are significantly greater than the reference transfer time and packet loss rate, QoE can be estimated to be “bad”. If the measured transfer time and packet loss rate are greater than the reference transfer time and packet loss rate, but not significantly greater, the QoE can be estimated to be “bad”. . If video test data cannot be received at terminal 10, the QoE can be estimated as “unusable”. If the estimated QoE is high, such as “very good” or “good”, the user can be encouraged to view a high quality video. On the other hand, if the estimated QoE is low, such as “bad”, the user can be encouraged to view a low quality video.

  FIG. 6 is a diagram illustrating an example of QoE estimation related to the application “IP phone”. The application “IP phone” can be said to be an application for a telephone service using IP (Internet Protocol).

  The QoE estimation for the application “IP phone” can be executed by the following steps 1 to 3.

  In step 1, the terminal 10 can request transmission of voice within the cloud 20. Specifically, the terminal 10 can issue a request for voice test data to the cloud 20. The test data may be part of normal audio data, as schematically shown in FIG. In other words, the test data may have a data size smaller than normal audio data. Test data may be specially prepared for QoE estimation.

  In step 2, the cloud 20 can transfer the requested voice data to the terminal 10. In other words, the terminal 10 can receive voice test data from the cloud 20.

  In step 3, the terminal 10 can measure the transfer time between the request and reception of voice test data at the terminal 10. The terminal 10 can also measure the packet loss rate during transfer.

  In step 4, the terminal 10 can estimate the QoE for the application “IP phone” based on the measured transfer time and packet loss rate. For example, the relationship between the measured transfer time and packet loss rate and the reference may be the basis for estimating QoE. If the measured transfer time and packet loss rate are well below the reference transfer time and packet loss rate, the QoE can be estimated to be “very good”. If the measured transfer time and packet loss rate are smaller than the reference transfer time and packet loss rate, but not sufficiently small, QoE can be estimated to be “good”. . Similarly, if the measured transfer time and packet loss rate are significantly greater than the reference transfer time and packet loss rate, QoE can be estimated to be “bad”. If the measured transfer time and packet loss rate are greater than the reference transfer time and packet loss rate, but not significantly greater, the QoE can be estimated to be “bad”. . If voice test data cannot be received at the terminal 10, the QoE can be estimated as “unusable”.

  Note that in the examples shown in FIGS. 4-6, any quality factor other than the transfer time and packet loss rate may be used. For example, the quality factor may be any factor related to QoS (Quality of Service) such as S / N. Further, variations in transfer time and packet loss rate may be used as quality factors. Similarly, standard deviation or variance of transfer time and packet loss rate may be used as a quality factor.

  FIG. 7 is a flowchart for illustrating an example of a method for setting a reference used in estimating QoE. This process routine can be executed by the terminal 10. This process routine can be initiated when a change of criteria is requested. When the application is terminated, the user can be prompted to decide whether to change the criteria. If the user feels that the output information regarding the QoE for the application that has just been terminated does not correspond to his / her experience, the user can change the criteria. It should be noted that any user data can be input to the terminal 10 via any user interface.

  In step 700, it can be determined whether a change in criteria is requested numerically. If the change in criteria is requested numerically, the process routine proceeds to step 702. Otherwise, the process routine proceeds to step 704.

  In step 702, a numerical value from the user can be input. In other words, the terminal 10 can receive a user input indicating a numerical value.

  In step 704, QoE from the user can be entered. In other words, the terminal 10 can receive user input indicating the QoE that the user is evaluating. The input QoE can be based on the actual experience of the user. In this method, QoE data from the user can be accumulated based on the execution of the application. QoE data from a user can be stored in a memory (database) for each application. In other words, QoE data can be stored to be associated with a corresponding application.

  In step 706, a statistical estimate can be performed based on the accumulated QoE data from the user. This statistical estimation can be performed to estimate the needs or preferences of the user of terminal 10. For example, this statistical estimation can be performed to determine which quality factors have the most impact on QoE.

  In step 708, the criteria can be updated (adjusted) based on the numerical values. For example, the criteria can be more strict or more lenient according to user input. The criteria can be updated (adjusted) based on statistical estimates. For example, if the statistical estimate indicates that the user is most concerned with speed in the application “video delivery”, the criteria related to transfer time can be made more stringent.

  FIG. 8 is a functional block diagram for illustrating an example of the configuration of the terminal 10. Note that the example shown in FIG. 8 can be implemented independently of the other examples described above.

  The terminal 10 can include a subsequent action prediction unit 102 and a QoE estimation triggering unit 104.

  The subsequent action prediction unit 102 can predict the user's subsequent actions. In other words, the subsequent action predicting unit 102 can predict which application (s) will be used later by the user of the terminal 10. For example, the subsequent action prediction unit 102 can predict which application (s) will be used within a predetermined time by the user of the terminal 10. The predetermined time may be set to be longer than the time required to perform the QoE estimation.

  The QoE estimation triggering unit 104 can determine the timing for executing the QoE estimation. The QoE estimation triggering unit 104 can trigger QoE estimation at a predetermined interval. The predetermined interval can be measured by timer 106. Further, the QoE estimation triggering unit 104 can trigger QoE estimation in response to a user command. The user command can be input via the user interface 108.

  Furthermore, the QoE estimation triggering unit 104 can trigger QoE estimation based on the prediction of the subsequent action prediction unit 102. In other words, the QoE estimation triggering unit 104 can determine for which application the QoE estimation is to be performed next, and determine the timing for starting the QoE estimation. The timing for triggering (starting) the QoE estimation for the application may be a timing at which information about the estimated QoE based on the QoE estimation is output before the application is actually executed.

  FIG. 9 is a flowchart for illustrating an example of operations of the subsequent action prediction unit 102 and the QoE estimation triggering unit 104.

  In step 902, the subsequent action prediction unit 102 can predict the order in which a plurality of applications will be executed on the terminal 10. The subsequent action prediction unit 102 can predict the order based on the statistical information 130. The statistical information 130 can be accumulated based on past user actions.

There may be a trend in the order in which multiple applications are executed on the terminal 10. For example, the following action patterns can be stored as the statistical information 130.
Action Pattern 1: Power On (or Activation)-Email (Receive)-Calendar-IP Phone-Map-Street View-IP Phone-Email (Receive);
Action pattern 2: Power on (or activation)-Email (receive)-Browser-Email (send)-Web album;
Action Pattern 3: Power On (or Activation)-Email (Receive)-Web Album-IP Phone-Browser-Calendar; and Action Pattern 4: Power On (or Activation)-Email (Receive)-IP Phone- Browser-Calendar.

  Such action patterns can be stored to be associated with a situation or environment such as time (morning, afternoon, night, weekday, weekend, holiday, etc.), location (outdoor, indoor, on a train, etc.) is there. In this case, the subsequent action predicting unit 102 can predict an action pattern based on the current situation or environment. Furthermore, the subsequent action prediction unit 102 can simply predict the action pattern detected most frequently.

  In step 904, the QoE estimation triggering unit 104 can determine the order of QoE estimation for each application according to the order predicted in step 902. In other words, the QoE estimation triggering unit 104 can trigger QoE estimation for each application according to the order predicted in step 902. For example, when the action pattern 1 is predicted, the QoE estimation triggering unit 104 triggers the QoE estimation for the application “E-mail” first, the QoE estimation for the application “Calendar” second, and so on. can do.

  The result of QoE estimation can be output on the terminal as described above.

  FIG. 10 is a flowchart for illustrating another example of the operations of the subsequent action prediction unit 102 and the QoE estimation triggering unit 104.

  In step 1002, the subsequent action prediction unit 102 can predict the probability that each application will be executed on the terminal 10.

  The subsequent action predicting unit 102 can predict the probability based on the statistical information 130. The statistical information 130 can be accumulated based on past user actions.

  For example, the subsequent action prediction unit 102 can simply predict the probability based on the frequency of use of each application. In this case, the subsequent action prediction unit 102 can simply predict the highest probability for the most frequently used application.

  The subsequent action predicting unit 102 can predict the probability based on the information 140 and the statistical information 130 regarding the currently used application. For example, if action patterns 1 to 4 are stored as statistical information 130, then after using the application “Email” to receive emails, then four applications: calendar, browser, web album , And IP phones may be used. Therefore, the subsequent action predicting unit 102 can increase the probability of these four applications. In this case, the subsequent action predicting unit 102 can determine the application having the highest probability among these applications by comparing the frequency of use among these four applications.

  The subsequent action prediction unit 102 can predict the probability based on the information 140 related to the e-mail content. These e-mail contents may be e-mail contents currently opened (displayed) on the terminal 10. In other words, the e-mail content may be the content of the e-mail currently being browsed by the user of the terminal 10. Email content may include date information, schedule inquiries, and the like. Email content may include web links. Email content may include an image file attached therein and a video file attached therein. The email content may include a message requesting a call with or without an IP phone number. These contents can be analyzed by the analyzer of the terminal 10. In this case, when the e-mail content includes date information, a schedule inquiry, etc., the subsequent action predicting unit 102 can predict that the probability related to the application “calendar” becomes high, for example. Similarly, when the e-mail content includes a web link, the subsequent action predicting unit 102 can predict that the probability related to the application “browser” becomes high, for example. When the e-mail content includes an image file, the subsequent action predicting unit 102 can predict that the probability related to the application “web album” becomes high, for example. When the e-mail content includes a video file, the subsequent action predicting unit 102 can predict that the probability related to the application “video distribution” becomes high, for example. If the e-mail content includes a message requesting a call, the subsequent action predicting unit 102 can predict that the probability related to the application “IP phone” is high, for example.

  The subsequent action predicting unit 102 can predict the probability based on the information 140 regarding the web content, as in the case of accompanying the e-mail content.

  The subsequent action predicting unit 102 can predict the probability based on the information 140 regarding the date and time. In the case of a weekend (ie, Saturday and Sunday), the subsequent action predicting unit 102 may, for example, use the applications “music”, “game”, “navigation”, “web album”, and “news (especially general, economic)”. It can be predicted that the probability regarding In the case of weekdays, the subsequent action predicting unit 102 can predict that the probability related to the application “news” becomes high, for example. In the morning, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “schedule”, “E-mail (reading)”, and “news (especially weather, traffic information)” will increase. In the afternoon or evening, the subsequent action predicting unit 102 can predict that the probability related to the application “E-mail (write)” becomes high, for example. In the case of night, the subsequent action predicting unit 102 predicts that the probability regarding the applications “music”, “game”, “navigation”, “web album”, and “news (especially, general, economy)” becomes high, for example. be able to.

  The subsequent action prediction unit 102 can predict the probability based on the information 120 from the sensor attached in the terminal 10. The information 120 from the sensor may be information indicating an environment where the terminal 10 is arranged. For example, the sensor may include at least one of a GPS receiver, an accelerometer (or vibration sensor), an illumination sensor, a microphone sensor (interface), a power connection sensor (interface), and an earphone connection sensor (interface). it can.

  For example, the subsequent action prediction unit 102 can determine whether the user of the terminal 10 is moving or stopped based on information from the GPS receiver. When the user is moving, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “E-mail” and “IPTV” are high, for example. On the other hand, when the user is moving, the subsequent action predicting unit 102 can predict that the probability related to the application “IP phone” becomes low, for example. When the user is stopped, the subsequent action prediction unit 102 can predict that the probabilities related to the applications “TV conference”, “browser (web)”, and “IP phone” are high, for example.

  Information from the GPS receiver and map information can be cross-referenced to infer the location of the user (ie, terminal 100). In other words, the map matching technique used in the field of car navigation can be used. When the user is on the train, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “IPTV”, “E-mail”, and “Twitter” are high, for example. When the user is walking along the road, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “IP phone”, “radio”, “map”, and “street view” are high, for example. . When the user is driving a car, the subsequent action predicting unit 102 can predict that the probability related to the application “navigation” becomes high, for example. When the user is in the library, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “E-mail” and “Map” are high, for example. On the other hand, when the user is in the library, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “IP phone” and “TV conference” are low, for example. If the user is at the bank, the subsequent action predictor 102 may predict that the probabilities for the applications “news (especially stocks, foreign exchange)”, “IP phone”, and “email” will increase, for example. it can. It should be noted that an accelerometer can be used as in the GPS receiver described above.

  The subsequent action prediction unit 102 can determine whether the user of the terminal 10 is in an outdoor environment, an indoor environment, or a dark environment. When the user is in an outdoor environment, the subsequent action prediction unit 102 can predict that the probabilities related to the applications “navigation”, “map”, and “street view” will be high, for example. When the user is in an indoor environment, the subsequent action predicting unit 102 may predict that the probabilities related to the applications “browser”, “game”, “email”, “Twitter”, and “IPTV” are high, for example. it can. When the user is in a dark environment, the subsequent action predicting unit 102 can predict, for example, that the probability regarding the application “music” becomes high.

  The subsequent action predicting unit 102 can determine whether the user is in the train or driving a car based on the sound detected by the microphone sensor. When the user is on the train, the subsequent action predicting unit 102 can predict that the probabilities related to the applications “IPTV”, “E-mail”, and “Twitter” are high, for example. When the user is driving a car, the subsequent action predicting unit 102 can predict that the probability related to the application “navigation” becomes high, for example.

  Furthermore, when the microphone is connected to the terminal 10, the subsequent action prediction unit 102 can predict that the probability related to the application “IP phone” becomes high, for example. Similarly, when the earphone connection sensor detects that the earphone is connected to the terminal 10, the subsequent action prediction unit 102, for example, uses the applications “IPTV”, “IP phone”, “radio”, and “ It can be predicted that the probability of “music” will increase. Further, when the power connection sensor detects that the battery of the terminal 10 is being charged, the subsequent action prediction unit 102 can determine that the user is in the indoor environment, and thus For example, it can be predicted that the probabilities related to the applications “browser”, “game”, “email”, “Twitter”, and “IPTV” will increase.

  In step 1004, the QoE estimation triggering unit 104 can determine the order of QoE estimation for each application according to the probability predicted in step 902. In other words, the QoE estimation triggering unit 104 can trigger QoE estimation for each application according to the probability predicted in step 902. For example, the QoE estimation triggering unit 104 can trigger QoE estimation for each application in descending order of probability. Furthermore, the QoE estimation triggering unit 104 can stop triggering QoE estimation for applications (one or more) that have a probability less than a predetermined threshold. In other words, the QoE estimation triggering unit 104 can narrow down applications for which QoE estimation is executed.

  The result of QoE estimation can be output on the terminal as described above. For example, if information 140 about email content or web content is used to determine the probability, the results of the QoE estimation can be output on the email or website. In this case, the information regarding the estimated QoE includes the application corresponding to the status (eg, color) of at least one of a web link, an attached image file, an attached video file, an IP phone number, and the like. Can be output on the terminal 10 to be changed according to the estimated QoE. For example, if the estimated QoE for the application “browser” is “bad”, the color of the web link, which is usually blue, can be changed to “red”.

  Note that the above order and probabilities can be specified using any combination of the above elements. For example, the subsequent action prediction unit 102 may include statistical information 130, information 140 regarding currently used applications, information 140 regarding e-mail content, information 140 regarding date and time, and information 120 from sensors attached in the terminal 10. The probability that each application will be executed can be predicted based on any combination of.

  It can also be seen that the function of the subsequent action prediction unit 102 can be implemented by the cloud 20. For example, the cloud 20 stores statistical information related to the history of application operations of the entire user, and the next application based on information regarding both the behavior specific to the user of the terminal 10 and the behavior specific to the user in general. And its operation can be predicted.

  Some portions of the foregoing detailed description are presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory. These algorithmic descriptions or representations are examples of techniques used by standard engineers in the data processing arts to communicate the substance of their work to others skilled in the art. An algorithm is here and generally considered to be a consistent sequence of operations or similar processes that lead to a desired result. In this context, an operation or process includes a physical manipulation of physical quantities. Typically, although not required, such quantities are in the form of electrical or magnetic signals that can be stored, transferred, combined, compared, or otherwise manipulated. Can take. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, words, numbers, numbers or the like. It should be understood, however, that all of these and similar terms are associated with the appropriate physical quantities and are merely labels for convenience. Unless otherwise stated, as will be apparent from the following discussion, discussions that use terms such as “process,” “calculate,” “calculate,” and “determine” throughout this specification. Computing that manipulates or transforms data represented as physical, electronic, or magnetic quantities in a memory, register, or other information storage, transmission, or display device of a computing device Note that it refers to a device action or process.

  In the foregoing detailed description, various embodiments of apparatus and / or processes have been described through the use of block diagrams, flowcharts, and / or examples. As long as such a block diagram, flowchart, and / or example includes one or more functions and / or operations, each function and / or operation in such a block diagram, flowchart, or example may include: Those skilled in the art will appreciate that a wide range of hardware, software, firmware, or virtually any combination thereof can be implemented individually and / or collectively. In some embodiments, some portions of the subject matter described herein include application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), or other integrations. It can be implemented according to the conversion method. However, some aspects of the embodiments disclosed herein may be in whole or in part as one or more computer programs (eg, one or more) running on one or more computers. As one or more programs running on one computer system), as one or more programs running on one or more processors (eg, one or more running on one or more microprocessors) Those skilled in the art will recognize that they can be equivalently implemented in an integrated circuit (as multiple programs), as firmware, or virtually any combination thereof, as well as electrical circuit design and / or software and / or Or firmware coding Rashi and that it is well within the skill of the art, the skilled artisan will recognize. Further, those skilled in the art will appreciate that the mechanisms of the subject matter described herein can be distributed as various types of program products, and exemplary embodiments of the subject matter described herein. Will be understood regardless of the specific type of signaling medium used to actually perform the distribution. Examples of signal transmission media include recordable type media such as flexible disks, hard disk drives (HDDs), compact disks (CDs), digital versatile disks (DVDs), digital tapes, computer memories, and digital communications. Communication type media include, but are not limited to, media and / or analog communication media (eg, fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

  The subject matter described herein often illustrates various components, which are encompassed by or otherwise connected to various other components. It will be appreciated that the architecture so illustrated is merely an example, and in practice many other architectures that implement the same functionality can be implemented. In a conceptual sense, any configuration of components that achieve the same function is effectively “associated” to achieve the desired function. Thus, any two components herein combined to achieve a particular function are “associated” with each other so that the desired function is achieved, regardless of architecture or intermediate components. You can see that. Similarly, any two components so associated may be considered “operably connected” or “operably coupled” to each other to achieve the desired functionality, and as such Any two components that can be associated with can also be considered “operably coupled” to each other to achieve the desired functionality. Examples where it can be operatively coupled include physically interlockable and / or physically interacting components, and / or wirelessly interacting and / or wirelessly interacting components, And / or components that interact logically and / or logically interact with each other.

  For the use of substantially all plural and / or singular terms herein, those skilled in the art will recognize from the plural to the singular and / or singular as appropriate to the situation and / or application. You can convert from shape to plural. Various singular / plural permutations can be clearly described herein for ease of understanding.

  In general, terms used herein, particularly in the appended claims (eg, the body of the appended claims), are intended throughout as “open” terms. Will be understood by those skilled in the art (eg, the term “including” should be construed as “including but not limited to” and the term “having”). Should be interpreted as “having at least,” and the term “includes” should be interpreted as “including but not limited to”. ,Such). Where a specific number of statements is intended in the claims to be introduced, such intentions will be explicitly stated in the claims, and in the absence of such statements, such intentions It will be further appreciated by those skilled in the art that is not present. For example, as an aid to understanding, the appended claims use the introductory phrases “at least one” and “one or more” to guide the claim description. May include that. However, the use of such phrases may be used even if the same claim contains indefinite articles such as the introductory phrases “one or more” or “at least one” and “a” or “an”. The introduction of a claim statement by the indefinite article “a” or “an” shall include any particular claim, including the claim statement so introduced, to an invention containing only one such statement. Should not be construed as suggesting limiting (eg, “a” and / or “an” are to be interpreted as generally meaning “at least one” or “one or more”. It should be). The same applies to the use of definite articles used to introduce claim recitations. Further, even if a specific number is explicitly stated in the description of the claim to be introduced, such a description should normally be interpreted to mean at least the stated number, As will be appreciated by those skilled in the art (e.g., mere description of "two descriptions" without other modifiers usually means at least two descriptions, or more than one description). Further, in cases where a conventional expression similar to “at least one of A, B and C, etc.” is used, such syntax usually means that one skilled in the art would understand the conventional expression. Contemplated (eg, “a system having at least one of A, B, and C” means A only, B only, C only, A and B together, A and C together, B and C together And / or systems having both A, B, and C together, etc.). In cases where a customary expression similar to “at least one of A, B, or C, etc.” is used, such syntax is usually intended in the sense that one skilled in the art would understand the customary expression. (Eg, “a system having at least one of A, B, or C” includes A only, B only, C only, A and B together, A and C together, B and C together, And / or systems having both A, B, and C together, etc.). Any disjunctive word and / or phrase that presents two or more alternative terms may be either one of the terms, anywhere in the specification, claims, or drawings. It will be further understood by those skilled in the art that it should be understood that the possibility of including either of the terms (both terms), or both of them. For example, it will be understood that the phrase “A or B” includes the possibilities of “A” or “B” or “A and B”.

  Although various exemplary techniques have been described and illustrated herein using various methods and systems, various other modifications can be made without departing from the claimed subject matter. Those skilled in the art will appreciate that equivalents can be substituted. In addition, many modifications may be made to adapt a particular situation to the teachings of claimed subject matter without departing from the core concept described herein. Accordingly, the claimed subject matter is not limited to the specific examples disclosed, and such claimed subject matter is within the scope of the appended claims and their equivalents. It is intended that all embodiments that fit can also be included.

Claims (14)

A method of outputting information on estimated QoE on a terminal that allows a plurality of applications to be executed,
Predicting a first application to be executed first on the terminal based on at least one of information and statistical information from sensors installed in the terminal;
Performing QoE estimation for the first application before the first application is actually executed, the QoE estimation requesting test data associated with the first application. Publishing to the cloud, receiving the test data from the cloud, measuring a quality factor based on the received test data, and between the measured quality factor and a corresponding criterion Executed by estimating QoE based on a relationship, wherein the criterion is set based on user data of the terminal;
Outputting information on the estimated QoE for the first application on the terminal based on the QoE estimation for the first application;
A second application to be executed next to the first application on the terminal based on at least one of the information and the statistical information from a sensor installed in the terminal; To predict,
A request to perform QoE estimation for the second application after the QoE estimation for the first application is performed, the QoE estimation determining test data associated with the second application Between the cloud, receiving the test data from the cloud, measuring a quality factor based on the received test data, and between the measured quality factor and a corresponding criterion Based on the relationship in step (i), wherein the criterion is set based on the user data of the terminal, and based on the QoE estimate for the second application, the second Information about estimated QoE for other applications Output information on the terminal,
Including said method. A method of outputting information on estimated QoE on a terminal on which a plurality of applications can be executed,
Performing QoE estimation for each of the plurality of applications; and
Outputting information on the estimated QoE on the terminal based on the QoE estimation;
The QoE estimation issues a request for test data to the cloud; receives the test data from the cloud; measures at least one quality factor based on the received test data; and the measurement and quality factor that is being performed by estimating the QoE based on the relationship between the reference, the reference is set on the basis of the user data of the terminal, method. The application includes an application for browsing an image in the cloud, the test data is image data, and the quality factor associated with the application for browsing the image is in the terminal. The method of claim 2 , comprising a transfer time between the request and reception of the image data. The application includes an application for viewing a video in the cloud, the test data is video data, and the quality factor associated with the application for viewing the video is two factors The method of claim 2 , wherein the factors are a transfer time between the request and reception of the video data at the terminal and a packet loss rate during the transfer of the video data. The application includes an application for IP phone over the cloud, the test data is voice data, and the quality factor associated with the application for IP phone includes two factors 3. The method of claim 2 , wherein the factors are a transfer time between the request and reception of the voice data at the terminal and a packet loss rate during the transfer of the voice data. A method of outputting information on estimated QoE on a terminal on which a plurality of applications can be executed,
Performing QoE estimation for each of the plurality of applications; and
Outputting information on the estimated QoE on the terminal based on the QoE estimation;
Outputting the information about the estimated QoE on the terminal includes changing a color of an icon displayed on the terminal according to the QoE estimation, each icon starting the corresponding application The method that is provided to you. A method of outputting information on estimated QoE on a terminal on which a plurality of applications can be executed,
Performing QoE estimation for each of the plurality of applications; and
Outputting information on the estimated QoE on the terminal based on the QoE estimation;
Predicting the order in which multiple applications will be executed on the terminal based on statistical information;
The order of the QoE estimation for each application is determined according to the predicted sequence method. The method according to claim 7 , wherein the statistical information is information indicating a history of use of the application. A method of outputting information on estimated QoE on a terminal on which a plurality of applications can be executed,
Performing QoE estimation for each of the plurality of applications; and
Outputting information on the estimated QoE on the terminal based on the QoE estimation;
Predicting the probability that the respective application will be executed on the terminal based on information from sensors installed in the terminal;
Wherein the application that is to QoE estimation is performed is determined according to the predicted probability methods. The method according to claim 9 , wherein the information from a sensor is information indicating an environment in which the terminal is arranged. The method of claim 10 , wherein the sensor comprises at least one of a GPS receiver, an accelerometer, a microphone sensor, an illumination sensor, a power connection sensor, and an earphone connection sensor. A method of outputting information on estimated QoE on a terminal on which a plurality of applications can be executed,
Performing QoE estimation for each of the plurality of applications; and
Outputting information on the estimated QoE on the terminal based on the QoE estimation;
Further comprising predicting a probability that the respective application will be executed on the terminal based on information currently displayed on the terminal, wherein the application on which the QoE estimation is to be performed includes: A method determined according to the predicted probability. The information currently displayed on the terminal includes at least one of a web link, an attached image file, an attached video file, an IP phone number, and an email address;
The method of claim 12 , wherein the QoE estimation is performed for the application associated with the information currently displayed on the terminal. Outputting the information about the estimated QoE on the terminal includes the web link, the attached image file, the attached video file, the IP phone number, and the email address. 14. The method of claim 13 , comprising changing at least one status according to the QoE estimate for the corresponding application.